ArticlePloS one2023
Development of a neural network model to predict the presence of fentanyl in community drug samples.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026Review
- Neural Network-Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy.Drug testing and analysis · 2026Article
- Temporal and regional associations between fentanyl concentrations in the unregulated drug supply and drug-related mortality in British Columbia, Canada.The International journal on drug policy · 2026Article
- Development and validation of a fentanyl quantification model for drug checking services.Drug and alcohol dependence reports · 2026Article
- Detection of benzodiazepines in the unregulated drug supply using point of care and confirmatory drug checking technologies: A validation study.Drug and alcohol dependence · 2025Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIncreasingly, Fourier-transform infrared (FTIR) spectroscopy is being used as a harm reduction tool to provide people who use drugs real-time information about the contents of their substances. However, FTIR spectroscopy has been shown to have a high detection limit for fentanyl and interpretation of results by a technician can be subjective. This poses concern, given that some synthetic opioids can produce serious toxicity at sub-detectable levels. The objective of this study was to develop a neural network model to identify fentanyl and related analogues more accurately in drug samples compared to traditional analysis by technicians.
methodsData were drawn from samples analyzed point-of-care using combination FTIR spectroscopy and fentanyl immunoassay strips in British Columbia between August 2018 and January 2021. We developed neural network models to predict the presence of fentanyl based on FTIR data. The final model was validated against the results from immunoassay strips. Prediction performance was assessed using F1 score, accuracy, and area under the receiver-operating characteristic curve (AUROC), and was compared to results obtained from analysis by technicians.
resultsA total of 12,684 samples were included. The neural network model outperformed results from those analyzed by technicians, with an F1 score of 96.4% and an accuracy of 96.4%, compared to 78.4% and 82.4% with a technician, respectively. The AUROC of the model was 99.0%. Fentanyl positive samples correctly detected by the model but not by the technician were typically those with low fentanyl concentrations (median: 2.3% quantity by weight; quartile 1-3: 0.0%-4.6%). DISCUSSION: Neural network models can accurately predict the presence of fentanyl and related analogues using FTIR data, including samples with low fentanyl concentrations. Integrating this tool within drug checking services utilizing FTIR spectroscopy has the potential to improve decision making to reduce the risk of overdose and other negative health outcomes.
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